SOTAVerified

Reinforcement Learning (RL)

Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.

Papers

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Showing 4551–4560 of 15113 papers

TitleStatusHype
A Mini Review on the utilization of Reinforcement Learning with OPC UA—0
A Mixture-of-Expert Approach to RL-based Dialogue Management—0
AMM: Adaptive Modularized Reinforcement Model for Multi-city Traffic Signal Control—0
AMO: Adaptive Motion Optimization for Hyper-Dexterous Humanoid Whole-Body Control—0
A Model-based Approach for Sample-efficient Multi-task Reinforcement Learning—0
A model-based approach to meta-Reinforcement Learning: Transformers and tree search—0
A Model-based Multi-Agent Personalized Short-Video Recommender System—0
A Model-Based Reinforcement Learning Approach for a Rare Disease Diagnostic Task—0
A Model-Based Reinforcement Learning Approach for PID Design—0
A Model-free Learning Algorithm for Infinite-horizon Average-reward MDPs with Near-optimal Regret—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PPGMean Normalized Performance0.76—Unverified
2PPOMean Normalized Performance0.58—Unverified